计算机科学
启发式
管道(软件)
装箱问题
强化学习
产品(数学)
变量(数学)
自动化
工程制图
人工智能
工程类
算法
箱子
数学
机械工程
数学分析
程序设计语言
几何学
作者
Zifei Yang,Shuo Yang,Shuanglin Song,Wei Zhang,Ran Song,Jiyu Cheng,Yibin Li
标识
DOI:10.1109/iros51168.2021.9635914
摘要
Product packing is a typical application in ware-house automation that aims to pick objects from unstructured piles and place them into bins with optimized placing policy. However, it still remains a significant challenge to finish the product packing tasks in general logistics scenarios where the objects are variable-sized and the configurations are complex. In this work, we present the PackerBot, a complete robotic pipeline for performing variable-sized product packing in unstructured scenes. First, by leveraging the imperfect experience of human packer, we propose a heuristic DRL framework for learning optimal online 3D bin packing policy. Then we integrate it with a 6-DoF suction-based picking module and a product size estimation module, leading to a complete product packing system, namely the PackerBot. Extensive experimental results show that our method achieves the state-of-the-art performance in both simulated and real-world tests. The video demonstration is available at: https://vsislab.github.io/packerbot.
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